Symptom burden in patients with cancer and thromboembolism: A population-based matched cohort study
Notice bibliographique
Résumé
Abstract Background: Arterial (ATE) and venous thromboembolism (VTE) are common complications of cancer, but their impact on cancer symptom assessment and symptom burden is unknown. We sought to measure and compare the uptake of routine symptom assessment at Regional Cancer Centres (RCC) and to examine symptom burden progression in patients with newly diagnosed cancer and ATE or VTE versus matched controls. Methods: We conducted a population-based, retrospective matched cohort study in Ontario, Canada including adults (≥18 years) with a new cancer diagnosis (2007–2021). In two separate cohorts, patients with ATE or VTE within 6 months of diagnosis were matched to controls (1:5) based on age, sex, and cancer site. Basal/squamous cell carcinomas were excluded. The index date was the date of cancer diagnosis plus 6 months. Follow-up continued for 1 year or until death, non-residence in Ontario, or study end. Exposures were ATE or VTE, identified using validated ICD-9 or ICD-10 codes from hospitalizations or emergency department visits. The primary outcome was completion of the Edmonton Symptom Assessment System (ESAS), a validated tool that assesses self-reported symptoms of cancer. We calculated ESAS rates per 100 person-days compared them using generalized linear models. Relative rates (RR) were estimated via Andersen Gill models adjusted for imbalanced baseline characteristics. We then implemented a Markov multistate model to examine symptom severity progression over time for five symptoms (pain, anxiety, depression, lack of wellbeing, shortness of breath) by classifying patients as: none (ESAS score=0), mild/moderate (ESAS score=1–6), severe (ESAS score=7–10), or death. Results: The ATE cohort included 7,039 patients with ATE and 35,195 controls (mean age 70 years, 36% female). The most common cancer sites were lung (19.0%), hematologic (18.7%), and colorectal (14.0%). Patients with ATE had a lower ESAS rate (0.31 vs. 0.38 per 100 person-days; RR 0.81) despite a higher RCC visit rate (2.11 vs. 1.96 per 100 person-days, relative rate [RR] 1.08). ATE (RR 0.89 95%CI 0.85-0.94) and high comorbidity burden (RR 0.74 95%CI 0.66-0.83) were associated with a lower ESAS rate, while systemic therapy increased the rate (RR 3.34 95%CI 3.22-3.46). Among 3,218 patients with ATE (21,591 assessments) and 18,060 controls (135,201 assessments), baseline states were similar between groups. However, patients with ATE had a higher odds of worsening severity in all states (none, mild/moderate, severe) and across all symptoms compared to controls. The VTE cohort included 3,002 patients with VTE and 15,010 controls (mean age 64 years, 54% female). The most common cancer sites were lung (19.1%), hematologic (15.3%), and colorectal (12.5%). Patients with VTE had a higher ESAS rate (0.62 vs. 0.43 per 100 person-days; RR 1.44) and a higher RCC visit rate (3.49 vs. 1.99 per 100 person-days; RR 1.75). Systemic therapy (RR 2.68 95%CI 2.53-2.84), radiation (RR 1.07 95%CI 1.02-1.13), and advanced stage (stage II RR 1.30 95%CI 1.20-1.41, stage III RR 1.67 95%CI 1.53-1.92, stage IV RR 2.39 95%CI 2.19-2.60) were associated with higher ESAS rates. Among 1,744 patients with VTE (15,016 assessments) and 8,390 controls (66,836 assessments), baseline states were generally worse for VTE patients, specifically for pain, lack of wellbeing, and shortness of breath. Patients with VTE had a higher odds of worsening severity in all states and across all symptoms compared to controls. Conclusion: In patients with newly diagnosed cancer, ATE was associated with a lower ESAS rate despite a higher RCC visit rate, while VTE was associated with a higher ESAS rate compared to controls. Patients with ATE or VTE had a higher odds of worsening symptom severity compared to controls, highlighting the need to improve symptom monitoring and management in patients with thrombotic complications of cancer. Further studies should explore contributors to lower assessment rates in patients with ATE and to optimize assessment strategies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».